Librerias y Módulos🐼📊🗺

!pip install cartopy
!pip install pycountry
!pip install geopandas
!pip install --upgrade plotly-express

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import cartopy.crs as ccrs
import cartopy.feature as cfeature

Renderizar el DataFrame con Pandas🐼

df = pd.read_csv('world_population.csv')
num_filas = len(df)

num_columnas = len(df.columns)
print("Número de filas:", num_filas)
print("Número de columnas:", num_columnas)

df

Número de filas: 234
Número de columnas: 17

	Rank	CCA3	Country/Territory	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
0	36	AFG	Afghanistan	Kabul	Asia	41128771	38972230	33753499	28189672	19542982	10694796	12486631	10752971	652230	63.0587	1.0257	0.52
1	138	ALB	Albania	Tirana	Europe	2842321	2866849	2882481	2913399	3182021	3295066	2941651	2324731	28748	98.8702	0.9957	0.04
2	34	DZA	Algeria	Algiers	Africa	44903225	43451666	39543154	35856344	30774621	25518074	18739378	13795915	2381741	18.8531	1.0164	0.56
3	213	ASM	American Samoa	Pago Pago	Oceania	44273	46189	51368	54849	58230	47818	32886	27075	199	222.4774	0.9831	0.00
4	203	AND	Andorra	Andorra la Vella	Europe	79824	77700	71746	71519	66097	53569	35611	19860	468	170.5641	1.0100	0.00
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
229	226	WLF	Wallis and Futuna	Mata-Utu	Oceania	11572	11655	12182	13142	14723	13454	11315	9377	142	81.4930	0.9953	0.00
230	172	ESH	Western Sahara	El Aaiún	Africa	575986	556048	491824	413296	270375	178529	116775	76371	266000	2.1654	1.0184	0.01
231	46	YEM	Yemen	Sanaa	Asia	33696614	32284046	28516545	24743946	18628700	13375121	9204938	6843607	527968	63.8232	1.0217	0.42
232	63	ZMB	Zambia	Lusaka	Africa	20017675	18927715	16248230	13792086	9891136	7686401	5720438	4281671	752612	26.5976	1.0280	0.25
233	74	ZWE	Zimbabwe	Harare	Africa	16320537	15669666	14154937	12839771	11834676	10113893	7049926	5202918	390757	41.7665	1.0204	0.20

234 rows × 17 columns

import pandas as pd
import pycountry
def get_alpha_3(location):
  try:
    return pycountry.countries.get(name=location).alpha_3
  except:
    return None
df['Code']=df['Country/Territory'].apply(lambda x: get_alpha_3(x))
print(df.head(6))

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Cell In[10], line 2
      1 import pandas as pd
----> 2 import pycountry
      3 def get_alpha_3(location):
      4   try:

ModuleNotFoundError: No module named 'pycountry'

import plotly.express as px
figura=px.choropleth(df,locations='Code',
                     color='Area (km²)',hover_name='Country/Territory',
                     color_continuous_scale=px.colors.sequential.Plasma)
figura.show()

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[11], line 2
      1 import plotly.express as px
----> 2 figura=px.choropleth(df,locations='Code',
      3                      color='Area (km²)',hover_name='Country/Territory',
      4                      color_continuous_scale=px.colors.sequential.Plasma)
      5 figura.show()

File C:\ProgramData\anaconda3\Lib\site-packages\plotly\express\_chart_types.py:1091, in choropleth(data_frame, lat, lon, locations, locationmode, geojson, featureidkey, color, facet_row, facet_col, facet_col_wrap, facet_row_spacing, facet_col_spacing, hover_name, hover_data, custom_data, animation_frame, animation_group, category_orders, labels, color_discrete_sequence, color_discrete_map, color_continuous_scale, range_color, color_continuous_midpoint, projection, scope, center, fitbounds, basemap_visible, title, template, width, height)
   1051 def choropleth(
   1052     data_frame=None,
   1053     lat=None,
   (...)
   1085     height=None,
   1086 ) -> go.Figure:
   1087     """
   1088     In a choropleth map, each row of `data_frame` is represented by a
   1089     colored region mark on a map.
   1090     """
-> 1091     return make_figure(
   1092         args=locals(),
   1093         constructor=go.Choropleth,
   1094         trace_patch=dict(locationmode=locationmode),
   1095     )

File C:\ProgramData\anaconda3\Lib\site-packages\plotly\express\_core.py:1990, in make_figure(args, constructor, trace_patch, layout_patch)
   1987 layout_patch = layout_patch or {}
   1988 apply_default_cascade(args)
-> 1990 args = build_dataframe(args, constructor)
   1991 if constructor in [go.Treemap, go.Sunburst, go.Icicle] and args["path"] is not None:
   1992     args = process_dataframe_hierarchy(args)

File C:\ProgramData\anaconda3\Lib\site-packages\plotly\express\_core.py:1405, in build_dataframe(args, constructor)
   1402     args["color"] = None
   1403 # now that things have been prepped, we do the systematic rewriting of `args`
-> 1405 df_output, wide_id_vars = process_args_into_dataframe(
   1406     args, wide_mode, var_name, value_name
   1407 )
   1409 # now that `df_output` exists and `args` contains only references, we complete
   1410 # the special-case and wide-mode handling by further rewriting args and/or mutating
   1411 # df_output
   1413 count_name = _escape_col_name(df_output, "count", [var_name, value_name])

File C:\ProgramData\anaconda3\Lib\site-packages\plotly\express\_core.py:1207, in process_args_into_dataframe(args, wide_mode, var_name, value_name)
   1205         if argument == "index":
   1206             err_msg += "\n To use the index, pass it in directly as `df.index`."
-> 1207         raise ValueError(err_msg)
   1208 elif length and len(df_input[argument]) != length:
   1209     raise ValueError(
   1210         "All arguments should have the same length. "
   1211         "The length of column argument `df[%s]` is %d, whereas the "
   (...)
   1218         )
   1219     )

ValueError: Value of 'locations' is not the name of a column in 'data_frame'. Expected one of ['Rank', 'CCA3', 'Country/Territory', 'Capital', 'Continent', '2022 Population', '2020 Population', '2015 Population', '2010 Population', '2000 Population', '1990 Population', '1980 Population', '1970 Population', 'Area (km²)', 'Density (per km²)', 'Growth Rate', 'World Population Percentage'] but received: Code

import plotly.express as px
figura=px.choropleth(df,locations='Code',
                     color='Density (per km²)',hover_name='Country/Territory',
                     color_continuous_scale=px.colors.sequential.Plasma)
figura.show()

Estadística Descriptiva📊

estadisticas_descriptivas = df.describe() #generar estadísticas
estadisticas_descriptivas.rename(index={ #traducir a español
    'count': 'conteo',
    'mean': 'media',
    'std': 'desviación estándar',
    'min': 'mínimo',
    '25%': 'percentil 25',
    '50%': 'percentil 50 (mediana)',
    '75%': 'percentil 75',
    'max': 'máximo',
}, inplace=True)
# Eliminar notación científica con ceros
pd.options.display.float_format = '{:.2f}'.format
estadisticas_descriptivas

	Rank	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
conteo	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00	234.00
media	117.50	34074414.71	33501070.95	31729956.24	29845235.03	26269468.82	22710220.79	18984616.97	15786908.81	581449.38	452.13	1.01	0.43
desviación estándar	67.69	136766424.80	135589876.92	130404992.75	124218487.63	111698206.72	97832173.35	81785186.08	67795091.64	1761840.86	2066.12	0.01	1.71
mínimo	1.00	510.00	520.00	564.00	596.00	651.00	700.00	733.00	752.00	1.00	0.03	0.91	0.00
percentil 25	59.25	419738.50	415284.50	404676.00	393149.00	327242.00	264115.75	229614.25	155997.00	2650.00	38.42	1.00	0.01
percentil 50 (mediana)	117.50	5559944.50	5493074.50	5307400.00	4942770.50	4292907.00	3825409.50	3141145.50	2604830.00	81199.50	95.35	1.01	0.07
percentil 75	175.75	22476504.75	21447979.50	19730853.75	19159567.50	15762301.00	11869231.00	9826053.75	8817329.00	430425.75	238.93	1.02	0.28
máximo	234.00	1425887337.00	1424929781.00	1393715448.00	1348191368.00	1264099069.00	1153704252.00	982372466.00	822534450.00	17098242.00	23172.27	1.07	17.88

DataFrame🐼 de Europa 🗺

df_europa = df.loc[df['Continent'] == 'Europe']
#df_europa.describe()
#df_europa

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_europa[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Europa en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

DataFrame🐼 de Asia 🗺

df_asia = df.loc[df['Continent'] == 'Asia']
df_asia

	Rank	CCA3	Country/Territory	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
0	36	AFG	Afghanistan	Kabul	Asia	41128771	38972230	33753499	28189672	19542982	10694796	12486631	10752971	652230	63.06	1.03	0.52
9	140	ARM	Armenia	Yerevan	Asia	2780469	2805608	2878595	2946293	3168523	3556539	3135123	2534377	29743	93.48	1.00	0.03
13	91	AZE	Azerbaijan	Baku	Asia	10358074	10284951	9863480	9237202	8190337	7427836	6383060	5425317	86600	119.61	1.00	0.13
15	154	BHR	Bahrain	Manama	Asia	1472233	1477469	1362142	1213645	711442	517418	362595	222555	765	1924.49	1.01	0.02
16	8	BGD	Bangladesh	Dhaka	Asia	171186372	167420951	157830000	148391139	129193327	107147651	83929765	67541860	147570	1160.04	1.01	2.15
23	165	BTN	Bhutan	Thimphu	Asia	782455	772506	743274	705516	587207	558442	415257	298894	38394	20.38	1.01	0.01
29	175	BRN	Brunei	Bandar Seri Begawan	Asia	449002	441725	421437	396053	333926	261928	187921	133343	5765	77.88	1.01	0.01
33	73	KHM	Cambodia	Phnom Penh	Asia	16767842	16396860	15417523	14363532	12118841	8910808	6198959	6708525	181035	92.62	1.01	0.21
41	1	CHN	China	Beijing	Asia	1425887337	1424929781	1393715448	1348191368	1264099069	1153704252	982372466	822534450	9706961	146.89	1.00	17.88
73	131	GEO	Georgia	Tbilisi	Asia	3744385	3765912	3771132	3836831	4265172	5391636	5145843	4800426	69700	53.72	1.00	0.05
89	104	HKG	Hong Kong	Hong Kong	Asia	7488865	7500958	7399838	7132438	6731195	5838574	4978544	3955072	1104	6783.39	1.00	0.09
92	2	IND	India	New Delhi	Asia	1417173173	1396387127	1322866505	1240613620	1059633675	870452165	696828385	557501301	3287590	431.07	1.01	17.77
93	4	IDN	Indonesia	Jakarta	Asia	275501339	271857970	259091970	244016173	214072421	182159874	148177096	115228394	1904569	144.65	1.01	3.45
94	17	IRN	Iran	Tehran	Asia	88550570	87290193	81790841	75373855	65544383	55793629	38520664	28449705	1648195	53.73	1.01	1.11
95	35	IRQ	Iraq	Baghdad	Asia	44496122	42556984	37757813	31264875	24628858	17658381	13653369	9811347	438317	101.52	1.02	0.56
98	98	ISR	Israel	Jerusalem	Asia	9038309	8757489	8007778	7328445	6116958	4803254	3744608	2907307	20770	435.16	1.02	0.11
102	11	JPN	Japan	Tokyo	Asia	123951692	125244761	127250933	128105431	126803861	123686321	117624196	105416839	377930	327.98	0.99	1.55
104	83	JOR	Jordan	Amman	Asia	11285869	10928721	9494246	6931258	5056174	3480587	2216903	1557374	89342	126.32	1.01	0.14
105	66	KAZ	Kazakhstan	Nursultan	Asia	19397998	18979243	17835909	16627837	15236253	16866563	14172710	12265305	2724900	7.12	1.01	0.24
108	129	KWT	Kuwait	Kuwait City	Asia	4268873	4360444	3908743	2943356	1934901	1674938	1493870	802786	17818	239.58	1.00	0.05
109	110	KGZ	Kyrgyzstan	Bishkek	Asia	6630623	6424874	5914980	5483774	4935182	4394734	3691209	3016384	199951	33.16	1.02	0.08
110	103	LAO	Laos	Vientiane	Asia	7529475	7319399	6787419	6323418	5430853	4314443	3297519	2675283	236800	31.80	1.01	0.09
112	119	LBN	Lebanon	Beirut	Asia	5489739	5662923	6398940	4995800	4320642	3593700	2963702	2381791	10452	525.23	0.98	0.07
119	167	MAC	Macau	Concelho de Macau	Asia	695168	676283	615239	557297	431896	350227	245332	247284	30	23172.27	1.01	0.01
122	45	MYS	Malaysia	Kuala Lumpur	Asia	33938221	33199993	31068833	28717731	22945150	17517054	13215707	10306508	330803	102.59	1.01	0.43
123	174	MDV	Maldives	Malé	Asia	523787	514438	435582	361575	282507	224957	164887	123243	300	1745.96	1.00	0.01
135	134	MNG	Mongolia	Ulaanbaatar	Asia	3398366	3294335	2964749	2702520	2450979	2161433	1697780	1293880	1564110	2.17	1.02	0.04
140	26	MMR	Myanmar	Nay Pyi Taw	Asia	54179306	53423198	51483949	49390988	45538332	40099553	33465781	27284112	676578	80.08	1.01	0.68
143	49	NPL	Nepal	Kathmandu	Asia	30547580	29348627	27610325	27161567	24559500	19616530	15600442	12501285	147181	207.55	1.02	0.38
151	56	PRK	North Korea	Pyongyang	Asia	26069416	25867467	25258015	24686435	23367059	20799523	17973650	14996879	120538	216.28	1.00	0.33
155	127	OMN	Oman	Muscat	Asia	4576298	4543399	4191776	2881914	2344253	1804524	1017462	670693	309500	14.79	1.01	0.06
156	5	PAK	Pakistan	Islamabad	Asia	235824862	227196741	210969298	194454498	154369924	115414069	80624057	59290872	881912	267.40	1.02	2.96
158	122	PSE	Palestine	Ramallah	Asia	5250072	5019401	4484614	3992278	3139954	2124609	1453620	1118241	6220	844.06	1.02	0.07
163	13	PHL	Philippines	Manila	Asia	115559009	112190977	103031365	94636700	77958223	61558898	48419546	37435586	342353	337.54	1.01	1.45
167	143	QAT	Qatar	Doha	Asia	2695122	2760385	2414573	1713504	645937	441675	277450	118007	11586	232.62	1.00	0.03
182	41	SAU	Saudi Arabia	Riyadh	Asia	36408820	35997107	32749848	29411929	21547390	16004763	10171710	6106191	2149690	16.94	1.01	0.46
187	113	SGP	Singapore	Singapore	Asia	5975689	5909869	5650018	5163590	4053602	3022209	2400729	2061831	710	8416.46	1.01	0.07
194	29	KOR	South Korea	Seoul	Asia	51815810	51844690	50994401	48813042	46788591	44120039	38170501	32601143	100210	517.07	1.00	0.65
197	61	LKA	Sri Lanka	Colombo	Asia	21832143	21715079	21336697	20668557	18776371	17204094	14943645	12388769	65610	332.76	1.00	0.27
202	60	SYR	Syria	Damascus	Asia	22125249	20772595	19205178	22337563	16307654	12408996	8898954	6319199	185180	119.48	1.04	0.28
203	57	TWN	Taiwan	Taipei	Asia	23893394	23821464	23512136	23083083	22194731	20586174	18100281	14957870	36193	660.17	1.00	0.30
204	95	TJK	Tajikistan	Dushanbe	Asia	9952787	9543207	8524063	7621779	6272998	5417860	4045965	2993019	143100	69.55	1.02	0.12
206	20	THA	Thailand	Bangkok	Asia	71697030	71475664	70294397	68270489	63066603	55228410	45737753	35791728	513120	139.73	1.00	0.90
207	155	TLS	Timor-Leste	Dili	Asia	1341296	1299995	1205813	1088486	878360	758106	642224	554021	14874	90.18	1.02	0.02
213	18	TUR	Turkey	Ankara	Asia	85341241	84135428	79646178	73195345	64113547	54324142	44089069	35540990	783562	108.91	1.01	1.07
214	111	TKM	Turkmenistan	Ashgabat	Asia	6430770	6250438	5766431	5267970	4569132	3720278	2862903	2201432	488100	13.18	1.01	0.08
219	97	ARE	United Arab Emirates	Abu Dhabi	Asia	9441129	9287289	8916899	8481771	3275333	1900151	1014048	298084	83600	112.93	1.01	0.12
224	43	UZB	Uzbekistan	Tashkent	Asia	34627652	33526656	30949417	28614227	24925554	20579100	15947129	12011361	447400	77.40	1.02	0.43
228	16	VNM	Vietnam	Hanoi	Asia	98186856	96648685	92191398	87411012	79001142	66912613	52968270	41928849	331212	296.45	1.01	1.23
231	46	YEM	Yemen	Sanaa	Asia	33696614	32284046	28516545	24743946	18628700	13375121	9204938	6843607	527968	63.82	1.02	0.42

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_asia[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Asia en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

!pip install plotly.express

Collecting plotly.express
  Downloading plotly_express-0.4.1-py2.py3-none-any.whl (2.9 kB)
Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (1.5.3)
Requirement already satisfied: plotly>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (5.15.0)
Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (0.14.1)
Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (1.11.4)
Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (0.5.6)
Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.10/dist-packages (from plotly.express) (1.25.2)
Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->plotly.express) (2.8.2)
Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.20.0->plotly.express) (2023.4)
Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5->plotly.express) (1.16.0)
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly>=4.1.0->plotly.express) (8.2.3)
Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from plotly>=4.1.0->plotly.express) (24.0)
Installing collected packages: plotly.express
Successfully installed plotly.express-0.4.1

import plotly.express as px
import pandas as pd
df_asia.rename(columns={"Country/Territory": "pais", "World Population Percentage": "Poblacion_mundial"}, inplace=True)
fig = px.line(df_asia, x="pais", y="Poblacion_mundial")
fig.show()

C:\Users\Juan Felipe\AppData\Local\Temp\ipykernel_11852\2786135486.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_asia.rename(columns={"Country/Territory": "pais", "World Population Percentage": "Poblacion_mundial"}, inplace=True)

DataFrame🐼 de Africa 🗺

df_africa = df.loc[df['Continent'] == 'Africa']
df_africa

	Rank	CCA3	Country/Territory	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
2	34	DZA	Algeria	Algiers	Africa	44903225	43451666	39543154	35856344	30774621	25518074	18739378	13795915	2381741	18.85	1.02	0.56
5	42	AGO	Angola	Luanda	Africa	35588987	33428485	28127721	23364185	16394062	11828638	8330047	6029700	1246700	28.55	1.03	0.45
21	77	BEN	Benin	Porto-Novo	Africa	13352864	12643123	10932783	9445710	6998023	5133419	3833939	3023443	112622	118.56	1.03	0.17
26	144	BWA	Botswana	Gaborone	Africa	2630296	2546402	2305171	2091664	1726985	1341474	938578	592244	582000	4.52	1.02	0.03
31	58	BFA	Burkina Faso	Ouagadougou	Africa	22673762	21522626	18718019	16116845	11882888	9131361	6932967	5611666	272967	83.06	1.03	0.28
32	78	BDI	Burundi	Bujumbura	Africa	12889576	12220227	10727148	9126605	6307659	5483793	4312834	3497834	27834	463.09	1.03	0.16
34	53	CMR	Cameroon	Yaounde	Africa	27914536	26491087	23012646	19878036	15091594	11430520	8519891	6452787	475442	58.71	1.03	0.35
36	171	CPV	Cape Verde	Praia	Africa	593149	582640	552166	521212	458251	364563	317234	287262	4033	147.07	1.01	0.01
38	117	CAF	Central African Republic	Bangui	Africa	5579144	5343020	4819333	4660067	3759170	2809221	2415276	2067356	622984	8.96	1.02	0.07
39	69	TCD	Chad	N'Djamena	Africa	17723315	16644701	14140274	11894727	8259137	5827069	4408230	3667394	1284000	13.80	1.03	0.22
43	163	COM	Comoros	Moroni	Africa	836774	806166	730216	656024	536758	431119	328328	242351	1862	449.40	1.02	0.01
52	160	DJI	Djibouti	Djibouti	Africa	1120849	1090156	1006259	919199	742033	577173	324121	144379	23200	48.31	1.01	0.01
55	15	COD	DR Congo	Kinshasa	Africa	99010212	92853164	78656904	66391257	48616317	35987541	26708686	20151733	2344858	42.22	1.03	1.24
57	14	EGY	Egypt	Cairo	Africa	110990103	107465134	97723799	87252413	71371371	57214630	43748556	34781986	1002450	110.72	1.02	1.39
59	152	GNQ	Equatorial Guinea	Malabo	Africa	1674908	1596049	1346973	1094524	684977	465549	282509	316955	28051	59.71	1.02	0.02
60	132	ERI	Eritrea	Asmara	Africa	3684032	3555868	3340006	3147727	2392880	2149960	1657982	1272748	117600	31.33	1.02	0.05
62	159	SWZ	Eswatini	Mbabane	Africa	1201670	1180655	1133936	1099920	1030496	854011	598564	442865	17364	69.20	1.01	0.02
63	12	ETH	Ethiopia	Addis Ababa	Africa	123379924	117190911	102471895	89237791	67031867	47878073	34945469	28308246	1104300	111.73	1.03	1.55
71	146	GAB	Gabon	Libreville	Africa	2388992	2292573	2028517	1711105	1272935	983028	749078	597192	267668	8.93	1.02	0.03
72	142	GMB	Gambia	Banjul	Africa	2705992	2573995	2253133	1937275	1437539	1040616	718586	528731	10689	253.16	1.02	0.03
75	47	GHA	Ghana	Accra	Africa	33475870	32180401	28870939	25574719	19665502	15446982	11865246	8861895	238533	140.34	1.02	0.42
84	75	GIN	Guinea	Conakry	Africa	13859341	13205153	11625998	10270728	8336967	6354145	4972609	4222374	245857	56.37	1.02	0.17
85	149	GNB	Guinea-Bissau	Bissau	Africa	2105566	2015828	1788919	1567220	1230849	973551	831462	591663	36125	58.29	1.02	0.03
100	52	CIV	Ivory Coast	Yamoussoukro	Africa	28160542	26811790	23596741	21120042	16799670	11910540	8303809	5477086	322463	87.33	1.02	0.35
106	27	KEN	Kenya	Nairobi	Africa	54027487	51985780	46851488	41517895	30851606	23162269	16187124	11473087	580367	93.09	1.02	0.68
113	147	LSO	Lesotho	Maseru	Africa	2305825	2254100	2118521	2022747	1998630	1798997	1407672	1023481	30355	75.96	1.01	0.03
114	121	LBR	Liberia	Monrovia	Africa	5302681	5087584	4612329	4019956	2895224	2209731	1932169	1463563	111369	47.61	1.02	0.07
115	107	LBY	Libya	Tripoli	Africa	6812341	6653942	6192235	6491988	5154790	4236983	2962720	1909177	1759540	3.87	1.01	0.09
120	50	MDG	Madagascar	Antananarivo	Africa	29611714	28225177	24850912	21731053	16216431	11882762	8948162	6639751	587041	50.44	1.02	0.37
121	62	MWI	Malawi	Lilongwe	Africa	20405317	19377061	16938942	14718422	11229387	9539665	6267369	4625141	118484	172.22	1.03	0.26
124	59	MLI	Mali	Bamako	Africa	22593590	21224040	18112907	15529181	11239101	8945026	7372581	6153587	1240192	18.22	1.03	0.28
128	126	MRT	Mauritania	Nouakchott	Africa	4736139	4498604	3946220	3419461	2695003	2006027	1506694	1122198	1030700	4.60	1.03	0.06
129	157	MUS	Mauritius	Port Louis	Africa	1299469	1297828	1293153	1283330	1215930	1090290	954865	830115	2040	636.99	1.00	0.02
130	182	MYT	Mayotte	Mamoudzou	Africa	326101	305587	249545	211786	159215	92659	52233	35383	374	871.93	1.03	0.00
138	40	MAR	Morocco	Rabat	Africa	37457971	36688772	34680458	32464865	28554415	24570814	19678444	15274351	446550	83.88	1.01	0.47
139	48	MOZ	Mozambique	Maputo	Africa	32969517	31178239	26843246	23073723	17768505	13303459	11413587	8411676	801590	41.13	1.03	0.41
141	145	NAM	Namibia	Windhoek	Africa	2567012	2489098	2282704	2099271	1819141	1369011	975994	754467	825615	3.11	1.01	0.03
148	54	NER	Niger	Niamey	Africa	26207977	24333639	20128124	16647543	11622665	8370647	6173177	4669708	1267000	20.69	1.04	0.33
149	6	NGA	Nigeria	Abuja	Africa	218541212	208327405	183995785	160952853	122851984	95214257	72951439	55569264	923768	236.58	1.02	2.74
168	114	COG	Republic of the Congo	Brazzaville	Africa	5970424	5702174	5064386	4437884	3134030	2385435	1829256	1396989	342000	17.46	1.02	0.07
169	161	REU	Reunion	Saint-Denis	Africa	974052	957822	922495	890130	785424	658992	551674	473925	2511	387.91	1.01	0.01
172	76	RWA	Rwanda	Kigali	Africa	13776698	13146362	11642959	10309031	8109989	7319962	5247532	3896367	26338	523.07	1.02	0.17
181	187	STP	Sao Tome and Principe	São Tomé	Africa	227380	218641	201124	182138	143714	120343	97210	77583	964	235.87	1.02	0.00
183	72	SEN	Senegal	Dakar	Africa	17316449	16436119	14356181	12530121	9704287	7536001	5703869	4367744	196722	88.03	1.03	0.22
185	196	SYC	Seychelles	Victoria	Africa	107118	105530	99240	92409	80060	71057	65290	54379	452	236.99	1.01	0.00
186	102	SLE	Sierra Leone	Freetown	Africa	8605718	8233969	7314773	6436698	4584067	4325388	3367477	2778557	71740	119.96	1.02	0.11
192	70	SOM	Somalia	Mogadishu	Africa	17597511	16537016	13763906	12026649	8721465	6999096	5892224	3720977	637657	27.60	1.03	0.22
193	24	ZAF	South Africa	Pretoria	Africa	59893885	58801927	55876504	51784921	46813266	39877570	29463549	22368306	1221037	49.05	1.01	0.75
195	86	SSD	South Sudan	Juba	Africa	10913164	10606227	11194299	9714419	6114440	4750817	4192011	3342410	619745	17.61	1.02	0.14
198	32	SDN	Sudan	Khartoum	Africa	46874204	44440486	38171178	33739933	26298773	21090886	16673586	11305206	1886068	24.85	1.03	0.59
205	22	TZA	Tanzania	Dodoma	Africa	65497748	61704518	52542823	45110527	34463704	26206012	19297659	13618192	945087	69.30	1.03	0.82
208	100	TGO	Togo	Lomé	Africa	8848699	8442580	7473229	6571855	5008035	3875947	2838110	2197383	56785	155.83	1.02	0.11
212	79	TUN	Tunisia	Tunis	Africa	12356117	12161723	11557779	10895063	9893316	8440023	6578156	5047404	163610	75.52	1.01	0.15
217	31	UGA	Uganda	Kampala	Africa	47249585	44404611	37477356	32341728	24020697	17586630	13284026	10317212	241550	195.61	1.03	0.59
230	172	ESH	Western Sahara	El Aaiún	Africa	575986	556048	491824	413296	270375	178529	116775	76371	266000	2.17	1.02	0.01
232	63	ZMB	Zambia	Lusaka	Africa	20017675	18927715	16248230	13792086	9891136	7686401	5720438	4281671	752612	26.60	1.03	0.25
233	74	ZWE	Zimbabwe	Harare	Africa	16320537	15669666	14154937	12839771	11834676	10113893	7049926	5202918	390757	41.77	1.02	0.20

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_africa[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Africa en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

DataFrame🐼 de Norte América 🗺

df_north_america = df.loc[df['Continent'] == 'North America']
df_north_america

	Rank	CCA3	Country/Territory	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage
6	224	AIA	Anguilla	The Valley	North America	15857	15585	14525	13172	11047	8316	6560	6283	91	174.25	1.01	0.00
7	201	ATG	Antigua and Barbuda	Saint John’s	North America	93763	92664	89941	85695	75055	63328	64888	64516	442	212.13	1.01	0.00
10	198	ABW	Aruba	Oranjestad	North America	106445	106585	104257	100341	89101	65712	62267	59106	180	591.36	1.00	0.00
14	176	BHS	Bahamas	Nassau	North America	409984	406471	392697	373272	325014	270679	223752	179129	13943	29.40	1.01	0.01
17	186	BRB	Barbados	Bridgetown	North America	281635	280693	278083	274711	264657	258868	253575	241397	430	654.97	1.00	0.00
20	177	BLZ	Belize	Belmopan	North America	405272	394921	359871	322106	240406	182589	145133	120905	22966	17.65	1.01	0.01
22	206	BMU	Bermuda	Hamilton	North America	64184	64031	63144	63447	61371	57470	53565	52019	54	1188.59	1.00	0.00
28	221	VGB	British Virgin Islands	Road Town	North America	31305	30910	29366	27556	20104	15617	11109	9581	151	207.32	1.01	0.00
35	39	CAN	Canada	Ottawa	North America	38454327	37888705	35732126	33963412	30683313	27657204	24511510	21434577	9984670	3.85	1.01	0.48
37	205	CYM	Cayman Islands	George Town	North America	68706	67311	60911	54074	39658	26027	17100	10533	264	260.25	1.01	0.00
45	124	CRI	Costa Rica	San José	North America	5180829	5123105	4895242	4622252	3979193	3158253	2414303	1855697	51100	101.39	1.01	0.06
47	85	CUB	Cuba	Havana	North America	11212191	11300698	11339894	11290417	11105791	10626680	9809107	8869636	109884	102.04	1.00	0.14
48	189	CUW	Curacao	Willemstad	North America	191163	189288	169572	159380	141424	155446	156851	150385	444	430.55	1.00	0.00
53	204	DMA	Dominica	Roseau	North America	72737	71995	70007	68755	68346	69481	72978	68895	751	96.85	1.00	0.00
54	84	DOM	Dominican Republic	Santo Domingo	North America	11228821	10999664	10405832	9775755	8540791	7129004	5755800	4475871	48671	230.71	1.01	0.14
58	112	SLV	El Salvador	San Salvador	North America	6336392	6292731	6231066	6114034	5958482	5367179	4508992	3619090	21041	301.14	1.00	0.08
78	208	GRL	Greenland	Nuuk	North America	56466	56026	55895	56351	56184	55599	50106	45434	2166086	0.03	1.00	0.00
79	193	GRD	Grenada	Saint George's	North America	125438	123663	118980	114039	107432	99047	94838	98794	344	364.65	1.01	0.00
80	178	GLP	Guadeloupe	Basse-Terre	North America	395752	395642	399089	403072	424067	391951	334234	318310	1628	243.09	1.00	0.00
82	68	GTM	Guatemala	Guatemala City	North America	17843908	17362718	16001107	14543121	11735894	9084780	6987767	5453208	108889	163.87	1.01	0.22
87	82	HTI	Haiti	Port-au-Prince	North America	11584996	11306801	10563757	9842880	8360225	6925331	5646676	4680812	27750	417.48	1.01	0.15
88	89	HND	Honduras	Tegucigalpa	North America	10432860	10121763	9294505	8450933	6656725	5053234	3777990	2782753	112492	92.74	1.01	0.13
101	139	JAM	Jamaica	Kingston	North America	2827377	2820436	2794445	2733896	2612205	2392030	2135546	1859091	10991	257.24	1.00	0.04
127	180	MTQ	Martinique	Fort-de-France	North America	367507	370391	383515	392181	432543	374271	333786	326428	1128	325.80	1.00	0.00
131	10	MEX	Mexico	Mexico City	North America	127504125	125998302	120149897	112532401	97873442	81720428	67705186	50289306	1964375	64.91	1.01	1.60
137	230	MSR	Montserrat	Brades	North America	4390	4500	5059	4938	5138	10805	11452	11402	102	43.04	0.99	0.00
147	106	NIC	Nicaragua	Managua	North America	6948392	6755895	6298598	5855734	5123222	4227820	3303309	2444767	130373	53.30	1.01	0.09
159	128	PAN	Panama	Panama City	North America	4408581	4294396	3957099	3623617	3001731	2449968	1956987	1516188	75417	58.46	1.01	0.06
166	136	PRI	Puerto Rico	San Juan	North America	3252407	3271564	3497335	3717922	3827108	3543776	3214568	2737619	8870	366.68	1.00	0.04
173	228	BLM	Saint Barthelemy	Gustavia	North America	10967	10681	9643	8988	7082	5168	2983	2417	21	522.24	1.01	0.00
174	211	KNA	Saint Kitts and Nevis	Basseterre	North America	47657	47642	47790	47403	45461	40636	43097	44968	261	182.59	1.00	0.00
175	190	LCA	Saint Lucia	Castries	North America	179857	179237	175623	170935	159500	142301	121633	103090	616	291.98	1.00	0.00
176	220	MAF	Saint Martin	Marigot	North America	31791	32552	35020	36458	29610	28127	7776	5802	53	599.83	1.00	0.00
177	229	SPM	Saint Pierre and Miquelon	Saint-Pierre	North America	5862	5906	5978	6052	6274	6324	6106	5537	242	24.22	1.00	0.00
178	199	VCT	Saint Vincent and the Grenadines	Kingstown	North America	103948	104632	106482	109308	113813	112487	107480	98459	389	267.22	1.00	0.00
188	214	SXM	Sint Maarten	Philipsburg	North America	44175	43621	40205	33034	30489	27845	12243	6260	34	1299.26	1.00	0.00
211	153	TTO	Trinidad and Tobago	Port-of-Spain	North America	1531044	1518147	1460177	1410296	1332203	1266518	1127852	988890	5130	298.45	1.00	0.02
215	212	TCA	Turks and Caicos Islands	Cockburn Town	North America	45703	44276	36538	29726	18744	11709	7598	5665	948	48.21	1.01	0.00
221	3	USA	United States	Washington, D.C.	North America	338289857	335942003	324607776	311182845	282398554	248083732	223140018	200328340	9372610	36.09	1.00	4.24
222	200	VIR	United States Virgin Islands	Charlotte Amalie	North America	99465	100442	102803	106142	108185	100685	96640	63446	347	286.64	0.99	0.00

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_north_america[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Norte America en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

DataFrame🐼 de Sur América 🗺

df_america_sur = df.loc[df['Continent'] == 'South America']
df_america_sur

	Rank	CCA3	Country/Territory	Capital	Continent	2022 Population	2020 Population	2015 Population	2010 Population	2000 Population	1990 Population	1980 Population	1970 Population	Area (km²)	Density (per km²)	Growth Rate	World Population Percentage	Code
8	33	ARG	Argentina	Buenos Aires	South America	45510318	45036032	43257065	41100123	37070774	32637657	28024803	23842803	2780400	16.37	1.01	0.57	ARG
24	80	BOL	Bolivia	Sucre	South America	12224110	11936162	11090085	10223270	8592656	7096194	5736088	4585693	1098581	11.13	1.01	0.15	None
27	7	BRA	Brazil	Brasilia	South America	215313498	213196304	205188205	196353492	175873720	150706446	122288383	96369875	8515767	25.28	1.00	2.70	BRA
40	65	CHL	Chile	Santiago	South America	19603733	19300315	17870124	17004162	15351799	13342868	11469828	9820481	756102	25.93	1.01	0.25	CHL
42	28	COL	Colombia	Bogota	South America	51874024	50930662	47119728	44816108	39215135	32601393	26176195	20905254	1141748	45.43	1.01	0.65	COL
56	67	ECU	Ecuador	Quito	South America	18001000	17588595	16195902	14989585	12626507	10449837	8135845	6172215	276841	65.02	1.01	0.23	ECU
64	231	FLK	Falkland Islands	Stanley	South America	3780	3747	3408	3187	3080	2332	2240	2274	12173	0.31	1.00	0.00	None
69	184	GUF	French Guiana	Cayenne	South America	304557	290969	257026	228453	164351	113931	66825	46484	83534	3.65	1.02	0.00	GUF
86	164	GUY	Guyana	Georgetown	South America	808726	797202	755031	747932	759051	747116	778176	705261	214969	3.76	1.01	0.01	GUY
161	109	PRY	Paraguay	Asunción	South America	6780744	6618695	6177950	5768613	5123819	4059195	3078912	2408787	406752	16.67	1.01	0.09	PRY
162	44	PER	Peru	Lima	South America	34049588	33304756	30711863	29229572	26654439	22109099	17492406	13562371	1285216	26.49	1.01	0.43	PER
199	170	SUR	Suriname	Paramaribo	South America	618040	607065	575475	546080	478998	412756	375112	379918	163820	3.77	1.01	0.01	SUR
223	133	URY	Uruguay	Montevideo	South America	3422794	3429086	3402818	3352651	3292224	3117012	2953750	2790265	181034	18.91	1.00	0.04	URY
227	51	VEN	Venezuela	Caracas	South America	28301696	28490453	30529716	28715022	24427729	19750579	15210443	11355475	916445	30.88	1.00	0.35	None

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_america_sur[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Sur america en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[27], line 10
      7 # Iterar sobre cada año y crear un gráfico para ese año
      8 for i, year in enumerate(years):
      9     # Seleccionar los datos correspondientes al año
---> 10     df_year = df_america_sur[['Country/Territory', f'{year} Population']]
     11     df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)
     13     # Crear el gráfico de barras para el año actual

NameError: name 'df_america_sur' is not defined

DataFrame🐼 de Oceanía 🗺

df_oceania = df.loc[df['Continent'] == 'Oceania']
df_oceania

# Crear una figura y ejes para los subgráficos
fig, axs = plt.subplots(8, 1, figsize=(20, 80), sharex=True)

# Lista de años
years = ['1970', '1980', '1990', '2000', '2010', '2015', '2020', '2022']

# Iterar sobre cada año y crear un gráfico para ese año
for i, year in enumerate(years):
    # Seleccionar los datos correspondientes al año
    df_year = df_oceania[['Country/Territory', f'{year} Population']]
    df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)

    # Crear el gráfico de barras para el año actual
    axs[i].barh(df_year_sorted['Country/Territory'], df_year_sorted[f'{year} Population'], color='skyblue')
    axs[i].set_xlabel(f'Población en {year}')
    axs[i].set_ylabel('País')
    axs[i].set_title(f'Población total de cada país en Oceania en {year}')
    axs[i].invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor

# Ajustar el espaciado entre los subgráficos
plt.tight_layout()

# Mostrar la figura
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[28], line 10
      7 # Iterar sobre cada año y crear un gráfico para ese año
      8 for i, year in enumerate(years):
      9     # Seleccionar los datos correspondientes al año
---> 10     df_year = df_oceania[['Country/Territory', f'{year} Population']]
     11     df_year_sorted = df_year.sort_values(by=f'{year} Population', ascending=False)
     13     # Crear el gráfico de barras para el año actual

NameError: name 'df_oceania' is not defined

Mapa Mundi 🗺

import plotly.express as px

df = px.data.gapminder().query("year==2007")
fig = px.choropleth(df, locations="iso_alpha",
                    color="lifeExp", # lifeExp is a column of gapminder
                    hover_name="country", # column to add to hover information
                    color_continuous_scale=px.colors.sequential.Plasma)
fig.show()

import plotly.express as px
import geopandas as gpd

df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
    px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")

fig = px.choropleth_mapbox(geo_df,
                           geojson=geo_df.geometry,
                           locations=geo_df.index,
                           color="Joly",
                           center={"lat": 45.5517, "lon": -73.7073},
                           mapbox_style="open-street-map",
                           zoom=8.5)
fig.show()

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

fig = go.Figure(data=go.Choropleth(
    locations = df['CODE'],
    z = df['GDP (BILLIONS)'],
    text = df['COUNTRY'],
    colorscale = 'Blues',
    autocolorscale=False,
    reversescale=True,
    marker_line_color='darkgray',
    marker_line_width=0.5,
    colorbar_tickprefix = '$',
    colorbar_title = 'GDP<br>Billions US$',
))

fig.update_layout(
    title_text='2014 Global GDP',
    geo=dict(
        showframe=False,
        showcoastlines=False,
        projection_type='equirectangular'
    ),
    annotations = [dict(
        x=0.55,
        y=0.1,
        xref='paper',
        yref='paper',
        text='Source: <a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">\
            CIA World Factbook</a>',
        showarrow = False
    )]
)

fig.show()

import pandas as pd
import plotly.express as px
import json

# Cargar los datos geográficos de América del Norte desde el archivo GeoJSON local
with open('north_america_map.json') as f:
    counties = json.load(f)

# Leer el archivo CSV con datos de población
df_north_america = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

# Crear el mapa coroplético
fig = px.choropleth(df_north_america, locations='Country/Territory', color='2022 Population',
                    color_continuous_scale="Viridis",
                    range_color=(0, 1000000),
                    scope="north america",
                    labels={'2022 Population':'Population'}
                   )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
Cell In[39], line 7
      5 # Cargar los datos geográficos de América del Norte desde el archivo GeoJSON local
      6 with open('north_america_map.json') as f:
----> 7     counties = json.load(f)
      9 # Leer el archivo CSV con datos de población
     10 df_north_america = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

File C:\ProgramData\anaconda3\Lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    274 def load(fp, *, cls=None, object_hook=None, parse_float=None,
    275         parse_int=None, parse_constant=None, object_pairs_hook=None, **kw):
    276     """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
    277     a JSON document) to a Python object.
    278 
   (...)
    291     kwarg; otherwise ``JSONDecoder`` is used.
    292     """
--> 293     return loads(fp.read(),
    294         cls=cls, object_hook=object_hook,
    295         parse_float=parse_float, parse_int=parse_int,
    296         parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)

File C:\ProgramData\anaconda3\Lib\encodings\cp1252.py:23, in IncrementalDecoder.decode(self, input, final)
     22 def decode(self, input, final=False):
---> 23     return codecs.charmap_decode(input,self.errors,decoding_table)[0]

UnicodeDecodeError: 'charmap' codec can't decode byte 0x8d in position 2143: character maps to <undefined>

!pip install plotly==5.13.0

Collecting plotly==5.13.0
  Downloading plotly-5.13.0-py2.py3-none-any.whl (15.2 MB)
     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 15.2/15.2 MB 45.1 MB/s eta 0:00:00
Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly==5.13.0) (8.2.3)
Installing collected packages: plotly
  Attempting uninstall: plotly
    Found existing installation: plotly 5.15.0
    Uninstalling plotly-5.15.0:
      Successfully uninstalled plotly-5.15.0
Successfully installed plotly-5.13.0

# Importar los módulos necesarios
import pandas as pd
import plotly.express as px
from urllib.request import urlopen
import json

# Cargar los datos geográficos de África desde el archivo GeoJSON
with urlopen('https://raw.githubusercontent.com/codeforamerica/click_that_hood/master/public/data/africa.geojson') as response:
    counties = json.load(response)

# Leer el archivo CSV con datos de población
df_africa = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

# Crear el mapa coroplético
fig = px.choropleth(df_africa, locations='Country/Territory', color='2022 Population',
                    color_continuous_scale="Viridis",
                    range_color=(0, 1000000),
                    scope="africa",
                    labels={'2022 Population':'Population'}
                   )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

import pandas as pd
import plotly.express as px
import json

# Cargar los datos geográficos de América del Norte desde el archivo GeoJSON local
with open('asia_map.json') as f:
    counties = json.load(f)

# Leer el archivo CSV con datos de población
df_asia = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

# Crear el mapa coroplético
fig = px.choropleth(df_asia, locations='Country/Territory', color='2022 Population',
                    color_continuous_scale="Viridis",
                    range_color=(0, 1000000),
                    scope="asia",
                    labels={'2022 Population':'Population'}
                   )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
Cell In[38], line 7
      5 # Cargar los datos geográficos de América del Norte desde el archivo GeoJSON local
      6 with open('asia_map.json') as f:
----> 7     counties = json.load(f)
      9 # Leer el archivo CSV con datos de población
     10 df_asia = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

File C:\ProgramData\anaconda3\Lib\json\__init__.py:293, in load(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)
    274 def load(fp, *, cls=None, object_hook=None, parse_float=None,
    275         parse_int=None, parse_constant=None, object_pairs_hook=None, **kw):
    276     """Deserialize ``fp`` (a ``.read()``-supporting file-like object containing
    277     a JSON document) to a Python object.
    278 
   (...)
    291     kwarg; otherwise ``JSONDecoder`` is used.
    292     """
--> 293     return loads(fp.read(),
    294         cls=cls, object_hook=object_hook,
    295         parse_float=parse_float, parse_int=parse_int,
    296         parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)

File C:\ProgramData\anaconda3\Lib\encodings\cp1252.py:23, in IncrementalDecoder.decode(self, input, final)
     22 def decode(self, input, final=False):
---> 23     return codecs.charmap_decode(input,self.errors,decoding_table)[0]

UnicodeDecodeError: 'charmap' codec can't decode byte 0x8d in position 2125: character maps to <undefined>

import plotly.graph_objects as go

fig = go.Figure(go.Scattergeo())
fig.update_geos(projection_type="orthographic")
fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

import plotly.graph_objects as go

fig = go.Figure(go.Scattergeo())
fig.update_geos(projection_type="natural earth")
fig.update_layout(height=300, margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

Testing Data & Graphics 🗺

# Definir el límite geográfico de Europa
europe_extent = [-20, 50, 35, 70]  # [longitud mínima, longitud máxima, latitud mínima, latitud máxima]

# Crear una figura y ejes
plt.figure(figsize=(14, 10))
ax = plt.axes(projection=ccrs.PlateCarree())

# Establecer el límite geográfico del mapa a Europa
ax.set_extent(europe_extent)

# Agregar contornos de los países de Europa
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5, edgecolor='black')

# Agregar detalles adicionales, como la costa
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)

# Mostrar el mapa de Europa
plt.show()

/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning:

Downloading: https://naturalearth.s3.amazonaws.com/50m_cultural/ne_50m_admin_0_boundary_lines_land.zip

/usr/local/lib/python3.10/dist-packages/cartopy/io/__init__.py:241: DownloadWarning:

Downloading: https://naturalearth.s3.amazonaws.com/50m_physical/ne_50m_coastline.zip

# Definir el límite geográfico de Europa
europe_extent = [-20, 50, 35, 70]  # [longitud mínima, longitud máxima, latitud mínima, latitud máxima]

# Crear una figura y ejes
plt.figure(figsize=(14, 10))
ax = plt.axes(projection=ccrs.PlateCarree())

# Establecer el límite geográfico del mapa a Europa
ax.set_extent(europe_extent)

# Agregar contornos de los países de Europa
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5, edgecolor='black')

# Agregar detalles adicionales, como la costa
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)

# Mostrar el mapa de Europa
plt.show()

# Definir el límite geográfico de América del Norte
north_america_extent = [-140, -50, 10, 80]  # [longitud mínima, longitud máxima, latitud mínima, latitud máxima]

# Crear una figura y ejes
plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())

# Establecer el límite geográfico del mapa a América del Norte
ax.set_extent(north_america_extent)

# Agregar contornos de los países de América del Norte
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5, edgecolor='black')

# Agregar detalles adicionales, como la costa
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)

# Mostrar el mapa de América del Norte
plt.show()

# Definir el límite geográfico de Asia
asia_extent = [30, 180, -10, 70]  # [longitud mínima, longitud máxima, latitud mínima, latitud máxima]

# Crear una figura y ejes
plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())

# Establecer el límite geográfico del mapa a Asia
ax.set_extent(asia_extent)

# Agregar contornos de los países de Asia
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5, edgecolor='black')

# Agregar detalles adicionales, como la costa
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)

# Mostrar el mapa de Asia
plt.show()

import pandas as pd
import plotly.express as px
from urllib.request import urlopen
import json

# Cargar los datos geográficos de Sudamérica desde el archivo GeoJSON
with urlopen('https://raw.githubusercontent.com/codeforamerica/click_that_hood/master/public/data/south-america.geojson') as response:
    counties = json.load(response)

# Leer el archivo CSV con datos de población
df_africa = pd.read_csv("world_population.csv", dtype={"Country/Territory": str})

# Crear el mapa coroplético
fig = px.choropleth(df_africa, locations='Country/Territory', color='2022 Population',
                    color_continuous_scale="Viridis",
                    range_color=(0, 1000000),
                    scope="south america",
                    labels={'2022 Population':'Population'}
                   )
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

# Definir el límite geográfico de Sudamérica
south_america_extent = [-85, -30, -60, 15]  # [longitud mínima, longitud máxima, latitud mínima, latitud máxima]

# Crear una figura y ejes
plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())

# Establecer el límite geográfico del mapa a Sudamérica
ax.set_extent(south_america_extent)

# Agregar contornos de los países de Sudamérica
ax.add_feature(cfeature.BORDERS, linestyle=':', linewidth=0.5, edgecolor='black')

# Agregar detalles adicionales, como la costa
ax.add_feature(cfeature.COASTLINE, linewidth=0.5)

# Mostrar el mapa de Sudamérica
plt.show()

plt.figure(figsize=(12, 8))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()


#plt.savefig('coastlines.pdf')
#plt.savefig('coastlines.png')

plt.show()

######################  1970  #########################
df_europa_1970 = df_europa[['Country/Territory', '1970 Population']]

# Ordenar por población en 2022 de mayor a menor
df_europa_1970_sorted = df_europa_1970.sort_values(by='1970 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_1970_sorted['Country/Territory'], df_europa_1970_sorted['1970 Population'], color='skyblue')
plt.barh(df_europa_1970_sorted['Country/Territory'], df_europa_1970_sorted['1970 Population'], color='skyblue')
plt.xlabel('Población en 1970')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 1970')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

######################  1980  #########################
df_europa_1980 = df_europa[['Country/Territory', '1980 Population']]

# Ordenar por población en 2022 de mayor a menor
df_europa_1980_sorted = df_europa_1980.sort_values(by='1980 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_1980_sorted['Country/Territory'], df_europa_1980_sorted['1980 Population'], color='skyblue')
plt.barh(df_europa_1980_sorted['Country/Territory'], df_europa_1980_sorted['1980 Population'], color='skyblue')
plt.xlabel('Población en 1980')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 1980')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

######################  1990  #########################
df_europa_1990 = df_europa[['Country/Territory', '1990 Population']]

# Ordenar por población en 2022 de mayor a menor
df_europa_1990_sorted = df_europa_1990.sort_values(by='1990 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_1990_sorted['Country/Territory'], df_europa_1990_sorted['1990 Population'], color='skyblue')
plt.barh(df_europa_1990_sorted['Country/Territory'], df_europa_1990_sorted['1990 Population'], color='skyblue')
plt.xlabel('Población en 1990')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 1990')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

######################  2000  #########################
df_europa_2000 = df_europa[['Country/Territory', '2000 Population']]

# Ordenar por población en 2022 de mayor a menor
df_europa_2000_sorted = df_europa_2000.sort_values(by='2000 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_2000_sorted['Country/Territory'], df_europa_2000_sorted['2000 Population'], color='skyblue')
plt.barh(df_europa_2000_sorted['Country/Territory'], df_europa_2000_sorted['2000 Population'], color='skyblue')
plt.xlabel('Población en 2000')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 1990')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

######################  2010  #########################
df_europa_2010 = df_europa[['Country/Territory', '2010 Population']]

# Ordenar por población en 2010 de mayor a menor
df_europa_2010_sorted = df_europa_2010.sort_values(by='2010 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_2010_sorted['Country/Territory'], df_europa_2010_sorted['2010 Population'], color='skyblue')
plt.barh(df_europa_2010_sorted['Country/Territory'], df_europa_2010_sorted['2010 Population'], color='skyblue')
plt.xlabel('Población en 2010')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 2010')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

######################  2015  #########################
df_europa_2015 = df_europa[['Country/Territory', '2015 Population']]

# Ordenar por población en 2010 de mayor a menor
df_europa_2015_sorted = df_europa_2015.sort_values(by='2015 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_2015_sorted['Country/Territory'], df_europa_2015_sorted['2015 Population'], color='skyblue')
plt.barh(df_europa_2015_sorted['Country/Territory'], df_europa_2015_sorted['2015 Population'], color='skyblue')
plt.xlabel('Población en 2015')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 2015')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()


# Ordenar por población en 2020 de mayor a menor
df_europa_2020 = df_europa[['Country/Territory', '2020 Population']]
df_europa_2020_sorted = df_europa_2020.sort_values(by='2020 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_2020_sorted['Country/Territory'], df_europa_2020_sorted['2020 Population'], color='skyblue')
plt.barh(df_europa_2020_sorted['Country/Territory'], df_europa_2020_sorted['2020 Population'], color='skyblue')
plt.xlabel('Población en 2020')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 2020')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()


######################  2022  #########################
df_europa_2022 = df_europa[['Country/Territory', '2022 Population']]

# Ordenar por población en 2022 de mayor a menor
df_europa_2022_sorted = df_europa_2022.sort_values(by='2022 Population', ascending=False)

# Crear el gráfico de barras
plt.figure(figsize=(20, 10))
plt.barh(df_europa_2022_sorted['Country/Territory'], df_europa_2022_sorted['2022 Population'], color='skyblue')
plt.barh(df_europa_2022_sorted['Country/Territory'], df_europa_2022_sorted['2022 Population'], color='skyblue')
plt.xlabel('Población en 2022')
plt.ylabel('País')
plt.title('Población total de cada país Europa en 2022')
plt.gca().invert_yaxis()  # Invertir el eje y para ordenar de mayor a menor
plt.show()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-9-29a875062c50> in <cell line: 2>()
      1 ######################  1970  #########################
----> 2 df_europa_1970 = df_europa[['Country/Territory', '1970 Population']]
      3 
      4 # Ordenar por población en 2022 de mayor a menor
      5 df_europa_1970_sorted = df_europa_1970.sort_values(by='1970 Population', ascending=False)

NameError: name 'df_europa' is not defined